Overview

Brought to you by YData

Dataset statistics

Number of variables44
Number of observations100000
Missing cells44799
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.6 MiB
Average record size in memory352.0 B

Variable types

Numeric20
Categorical16
Text5
DateTime3

Alerts

year has constant value "2018"Constant
num_subjects has constant value "1"Constant
Unnamed: 0 is highly overall correlated with Unnamed: 0.1High correlation
Unnamed: 0.1 is highly overall correlated with Unnamed: 0High correlation
avg_price_per_card is highly overall correlated with log_price and 4 other fieldsHigh correlation
brand_encoded is highly overall correlated with grade_x_brand and 1 other fieldsHigh correlation
card_number_len is highly overall correlated with card_number_numericHigh correlation
card_number_numeric is highly overall correlated with card_number_lenHigh correlation
days_between_sales is highly overall correlated with sale_count_per_card and 1 other fieldsHigh correlation
days_since_release is highly overall correlated with is_holiday_season and 1 other fieldsHigh correlation
grade is highly overall correlated with grade_is_high and 1 other fieldsHigh correlation
grade_is_high is highly overall correlated with grade and 1 other fieldsHigh correlation
grade_x_brand is highly overall correlated with brand_encoded and 1 other fieldsHigh correlation
grade_x_year is highly overall correlated with grade and 1 other fieldsHigh correlation
grading_company is highly overall correlated with grading_company_encoded and 2 other fieldsHigh correlation
grading_company_encoded is highly overall correlated with grading_company and 2 other fieldsHigh correlation
is_bgs is highly overall correlated with grading_company and 2 other fieldsHigh correlation
is_holiday_season is highly overall correlated with days_since_release and 1 other fieldsHigh correlation
is_outlier is highly overall correlated with log_price and 1 other fieldsHigh correlation
is_prizm is highly overall correlated with brand_encoded and 3 other fieldsHigh correlation
is_psa is highly overall correlated with grading_company and 2 other fieldsHigh correlation
is_silver is highly overall correlated with is_prizm and 1 other fieldsHigh correlation
is_trending_down is highly overall correlated with is_trending_up and 1 other fieldsHigh correlation
is_trending_up is highly overall correlated with is_trending_down and 1 other fieldsHigh correlation
log_price is highly overall correlated with avg_price_per_card and 4 other fieldsHigh correlation
month_sold is highly overall correlated with is_holiday_seasonHigh correlation
price is highly overall correlated with avg_price_per_card and 3 other fieldsHigh correlation
price_tier is highly overall correlated with avg_price_per_card and 4 other fieldsHigh correlation
sale_count_per_card is highly overall correlated with days_between_salesHigh correlation
std_price_per_card is highly overall correlated with avg_price_per_card and 5 other fieldsHigh correlation
trend_score is highly overall correlated with avg_price_per_card and 3 other fieldsHigh correlation
variety_encoded is highly overall correlated with is_prizm and 1 other fieldsHigh correlation
year_sold is highly overall correlated with days_since_releaseHigh correlation
grading_company is highly imbalanced (63.4%)Imbalance
grading_company_encoded is highly imbalanced (63.4%)Imbalance
is_gold is highly imbalanced (88.9%)Imbalance
is_auto is highly imbalanced (83.9%)Imbalance
grade_is_high is highly imbalanced (74.3%)Imbalance
variety has 44796 (44.8%) missing valuesMissing
price is highly skewed (γ1 = 260.7736196)Skewed
card_number_numeric is highly skewed (γ1 = 43.19944248)Skewed
avg_price_per_card is highly skewed (γ1 = 266.6633808)Skewed
std_price_per_card is highly skewed (γ1 = 83.23940342)Skewed
Unnamed: 0.1 is uniformly distributedUniform
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0.1 has unique valuesUnique
Unnamed: 0 has unique valuesUnique
card_number_numeric has 1430 (1.4%) zerosZeros
price_tier has 10553 (10.6%) zerosZeros
std_price_per_card has 7133 (7.1%) zerosZeros
trend_score has 7204 (7.2%) zerosZeros
days_between_sales has 7148 (7.1%) zerosZeros

Reproduction

Analysis started2025-10-06 12:04:08.932113
Analysis finished2025-10-06 12:05:54.297794
Duration1 minute and 45.37 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Unnamed: 0.1
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49999.5
Minimum0
Maximum99999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:05:54.432103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4999.95
Q124999.75
median49999.5
Q374999.25
95-th percentile94999.05
Maximum99999
Range99999
Interquartile range (IQR)49999.5

Descriptive statistics

Standard deviation28867.658
Coefficient of variation (CV)0.57735893
Kurtosis-1.2
Mean49999.5
Median Absolute Deviation (MAD)25000
Skewness0
Sum4.99995 × 109
Variance8.3334167 × 108
MonotonicityStrictly increasing
2025-10-06T12:05:54.600117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999831
 
< 0.1%
999821
 
< 0.1%
999811
 
< 0.1%
999801
 
< 0.1%
999791
 
< 0.1%
999781
 
< 0.1%
999771
 
< 0.1%
999761
 
< 0.1%
999751
 
< 0.1%
999741
 
< 0.1%
Other values (99990)99990
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
999991
< 0.1%
999981
< 0.1%
999971
< 0.1%
999961
< 0.1%
999951
< 0.1%
999941
< 0.1%
999931
< 0.1%
999921
< 0.1%
999911
< 0.1%
999901
< 0.1%

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49999.5
Minimum0
Maximum99999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:05:54.752714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4999.95
Q124999.75
median49999.5
Q374999.25
95-th percentile94999.05
Maximum99999
Range99999
Interquartile range (IQR)49999.5

Descriptive statistics

Standard deviation28867.658
Coefficient of variation (CV)0.57735893
Kurtosis-1.2
Mean49999.5
Median Absolute Deviation (MAD)25000
Skewness0
Sum4.99995 × 109
Variance8.3334167 × 108
MonotonicityStrictly increasing
2025-10-06T12:05:54.944002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999831
 
< 0.1%
999821
 
< 0.1%
999811
 
< 0.1%
999801
 
< 0.1%
999791
 
< 0.1%
999781
 
< 0.1%
999771
 
< 0.1%
999761
 
< 0.1%
999751
 
< 0.1%
999741
 
< 0.1%
Other values (99990)99990
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
999991
< 0.1%
999981
< 0.1%
999971
< 0.1%
999961
< 0.1%
999951
< 0.1%
999941
< 0.1%
999931
< 0.1%
999921
< 0.1%
999911
< 0.1%
999901
< 0.1%

year
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2018
100000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters400000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018100000
100.0%

Length

2025-10-06T12:05:55.073785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:05:55.137402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2018100000
100.0%

Most occurring characters

ValueCountFrequency (%)
2100000
25.0%
0100000
25.0%
1100000
25.0%
8100000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2100000
25.0%
0100000
25.0%
1100000
25.0%
8100000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2100000
25.0%
0100000
25.0%
1100000
25.0%
8100000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2100000
25.0%
0100000
25.0%
1100000
25.0%
8100000
25.0%

subject
Text

Distinct390
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2025-10-06T12:05:55.452292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length169
Median length61
Mean length13.56453
Min length4

Characters and Unicode

Total characters1356453
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)0.1%

Sample

1st rowdeandre ayton
2nd rowluka doncic
3rd rowshai gilgeous-alexander
4th rowtim hardaway
5th rowlebron james
ValueCountFrequency (%)
luka24105
 
11.1%
doncic24087
 
11.1%
young14989
 
6.9%
trae14970
 
6.9%
jr12115
 
5.6%
james6617
 
3.1%
michael6364
 
2.9%
lebron6356
 
2.9%
porter6349
 
2.9%
gilgeous-alexander6036
 
2.8%
Other values (621)94931
43.8%
2025-10-06T12:05:55.952269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a124912
 
9.2%
n117642
 
8.7%
116919
 
8.6%
e116047
 
8.6%
o101311
 
7.5%
r89179
 
6.6%
i82668
 
6.1%
l73751
 
5.4%
c66896
 
4.9%
u53278
 
3.9%
Other values (29)413850
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1356453
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a124912
 
9.2%
n117642
 
8.7%
116919
 
8.6%
e116047
 
8.6%
o101311
 
7.5%
r89179
 
6.6%
i82668
 
6.1%
l73751
 
5.4%
c66896
 
4.9%
u53278
 
3.9%
Other values (29)413850
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1356453
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a124912
 
9.2%
n117642
 
8.7%
116919
 
8.6%
e116047
 
8.6%
o101311
 
7.5%
r89179
 
6.6%
i82668
 
6.1%
l73751
 
5.4%
c66896
 
4.9%
u53278
 
3.9%
Other values (29)413850
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1356453
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a124912
 
9.2%
n117642
 
8.7%
116919
 
8.6%
e116047
 
8.6%
o101311
 
7.5%
r89179
 
6.6%
i82668
 
6.1%
l73751
 
5.4%
c66896
 
4.9%
u53278
 
3.9%
Other values (29)413850
30.5%

brand
Text

Distinct460
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2025-10-06T12:05:56.286646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length70
Median length69
Mean length16.91864
Min length4

Characters and Unicode

Total characters1691864
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)0.1%

Sample

1st rowpanini donruss optic
2nd rowpanini prizm
3rd rowpanini donruss optic
4th rowpanini prizm fast break autographs
5th rowpanini prizm get hyped!
ValueCountFrequency (%)
panini99375
39.5%
prizm50973
20.3%
donruss17682
 
7.0%
optic13813
 
5.5%
select6074
 
2.4%
chronicles5452
 
2.2%
revolution3876
 
1.5%
hoops3514
 
1.4%
contenders3201
 
1.3%
concourse2961
 
1.2%
Other values (447)44538
17.7%
2025-10-06T12:05:56.814527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i292011
17.3%
n251258
14.9%
p174530
10.3%
151459
9.0%
a124861
7.4%
r109588
 
6.5%
s85768
 
5.1%
o81379
 
4.8%
e68697
 
4.1%
m60026
 
3.5%
Other values (32)292287
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1691864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i292011
17.3%
n251258
14.9%
p174530
10.3%
151459
9.0%
a124861
7.4%
r109588
 
6.5%
s85768
 
5.1%
o81379
 
4.8%
e68697
 
4.1%
m60026
 
3.5%
Other values (32)292287
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1691864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i292011
17.3%
n251258
14.9%
p174530
10.3%
151459
9.0%
a124861
7.4%
r109588
 
6.5%
s85768
 
5.1%
o81379
 
4.8%
e68697
 
4.1%
m60026
 
3.5%
Other values (32)292287
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1691864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i292011
17.3%
n251258
14.9%
p174530
10.3%
151459
9.0%
a124861
7.4%
r109588
 
6.5%
s85768
 
5.1%
o81379
 
4.8%
e68697
 
4.1%
m60026
 
3.5%
Other values (32)292287
17.3%

variety
Text

Missing 

Distinct1767
Distinct (%)3.2%
Missing44796
Missing (%)44.8%
Memory size781.4 KiB
2025-10-06T12:05:57.255944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length46
Mean length13.458626
Min length3

Characters and Unicode

Total characters742970
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique716 ?
Unique (%)1.3%

Sample

1st rowsilver prizm
2nd rowshock
3rd rowgold prizm
4th rowpink ice prizm
5th rowgold
ValueCountFrequency (%)
prizm31261
24.5%
silver13568
 
10.6%
red6309
 
4.9%
blue6092
 
4.8%
green3644
 
2.9%
white3537
 
2.8%
and3264
 
2.6%
ice3257
 
2.6%
pink2979
 
2.3%
break2973
 
2.3%
Other values (1097)50774
39.8%
2025-10-06T12:05:58.295135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r83027
 
11.2%
e77335
 
10.4%
72455
 
9.8%
i71690
 
9.6%
p46726
 
6.3%
m37338
 
5.0%
l36300
 
4.9%
z33519
 
4.5%
s32025
 
4.3%
a29665
 
4.0%
Other values (36)222890
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)742970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r83027
 
11.2%
e77335
 
10.4%
72455
 
9.8%
i71690
 
9.6%
p46726
 
6.3%
m37338
 
5.0%
l36300
 
4.9%
z33519
 
4.5%
s32025
 
4.3%
a29665
 
4.0%
Other values (36)222890
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)742970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r83027
 
11.2%
e77335
 
10.4%
72455
 
9.8%
i71690
 
9.6%
p46726
 
6.3%
m37338
 
5.0%
l36300
 
4.9%
z33519
 
4.5%
s32025
 
4.3%
a29665
 
4.0%
Other values (36)222890
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)742970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r83027
 
11.2%
e77335
 
10.4%
72455
 
9.8%
i71690
 
9.6%
p46726
 
6.3%
m37338
 
5.0%
l36300
 
4.9%
z33519
 
4.5%
s32025
 
4.3%
a29665
 
4.0%
Other values (36)222890
30.0%
Distinct1122
Distinct (%)1.1%
Missing3
Missing (%)< 0.1%
Memory size781.4 KiB
2025-10-06T12:05:59.383821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.4983049
Min length1

Characters and Unicode

Total characters249823
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique448 ?
Unique (%)0.4%

Sample

1st row157
2nd row280
3rd row162
4th row28
5th row4
ValueCountFrequency (%)
2807422
 
7.4%
785464
 
5.5%
1774810
 
4.8%
323056
 
3.1%
1982828
 
2.8%
2792743
 
2.7%
32487
 
2.5%
1842371
 
2.4%
661918
 
1.9%
61797
 
1.8%
Other values (1112)65101
65.1%
2025-10-06T12:06:01.247197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
147940
19.2%
241964
16.8%
835255
14.1%
728215
11.3%
616920
 
6.8%
016768
 
6.7%
915393
 
6.2%
313970
 
5.6%
513747
 
5.5%
413239
 
5.3%
Other values (26)6412
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)249823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
147940
19.2%
241964
16.8%
835255
14.1%
728215
11.3%
616920
 
6.8%
016768
 
6.7%
915393
 
6.2%
313970
 
5.6%
513747
 
5.5%
413239
 
5.3%
Other values (26)6412
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)249823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
147940
19.2%
241964
16.8%
835255
14.1%
728215
11.3%
616920
 
6.8%
016768
 
6.7%
915393
 
6.2%
313970
 
5.6%
513747
 
5.5%
413239
 
5.3%
Other values (26)6412
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)249823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
147940
19.2%
241964
16.8%
835255
14.1%
728215
11.3%
616920
 
6.8%
016768
 
6.7%
915393
 
6.2%
313970
 
5.6%
513747
 
5.5%
413239
 
5.3%
Other values (26)6412
 
2.6%

date
Date

Distinct1168
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Minimum2018-05-16 00:00:00
Maximum2022-02-17 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-06T12:06:01.980741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:06:02.433128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

High correlation  Skewed 

Distinct11081
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean459.60025
Minimum0.99
Maximum4006600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:02.744934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.99
5-th percentile13
Q139.99
median95
Q3250
95-th percentile1313.044
Maximum4006600
Range4006599
Interquartile range (IQR)210.01

Descriptive statistics

Standard deviation13625.364
Coefficient of variation (CV)29.646119
Kurtosis75159.22
Mean459.60025
Median Absolute Deviation (MAD)69
Skewness260.77362
Sum45960025
Variance1.8565055 × 108
MonotonicityNot monotonic
2025-10-06T12:06:03.128216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001031
 
1.0%
501006
 
1.0%
40938
 
0.9%
30853
 
0.9%
35783
 
0.8%
45771
 
0.8%
75763
 
0.8%
60733
 
0.7%
26729
 
0.7%
150701
 
0.7%
Other values (11071)91692
91.7%
ValueCountFrequency (%)
0.995
 
< 0.1%
11
 
< 0.1%
1.041
 
< 0.1%
1.2515
< 0.1%
1.293
 
< 0.1%
1.31
 
< 0.1%
1.51
 
< 0.1%
1.71
 
< 0.1%
1.721
 
< 0.1%
1.742
 
< 0.1%
ValueCountFrequency (%)
40066001
< 0.1%
10387801
< 0.1%
6457501
< 0.1%
571687.21
< 0.1%
3100001
< 0.1%
2400001
< 0.1%
2040001
< 0.1%
1980301
< 0.1%
1500601
< 0.1%
1488301
< 0.1%

grade
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.505225
Minimum0
Maximum10.5
Zeros35
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:03.640566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q19
median10
Q310
95-th percentile10
Maximum10.5
Range10.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60858763
Coefficient of variation (CV)0.064026641
Kurtosis23.783401
Mean9.505225
Median Absolute Deviation (MAD)0
Skewness-2.4403791
Sum950522.5
Variance0.3703789
MonotonicityNot monotonic
2025-10-06T12:06:04.426734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1051306
51.3%
936324
36.3%
9.57999
 
8.0%
82835
 
2.8%
8.5962
 
1.0%
7277
 
0.3%
6115
 
0.1%
7.551
 
0.1%
545
 
< 0.1%
10.539
 
< 0.1%
Other values (6)47
 
< 0.1%
ValueCountFrequency (%)
035
 
< 0.1%
11
 
< 0.1%
31
 
< 0.1%
46
 
< 0.1%
545
 
< 0.1%
5.51
 
< 0.1%
6115
0.1%
6.53
 
< 0.1%
7277
0.3%
7.551
 
0.1%
ValueCountFrequency (%)
10.539
 
< 0.1%
1051306
51.3%
9.57999
 
8.0%
936324
36.3%
8.5962
 
1.0%
82835
 
2.8%
7.551
 
0.1%
7277
 
0.3%
6.53
 
< 0.1%
6115
 
0.1%

grading_company
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
psa
86729 
bgs
13054 
sgc
 
217

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpsa
2nd rowpsa
3rd rowpsa
4th rowbgs
5th rowpsa

Common Values

ValueCountFrequency (%)
psa86729
86.7%
bgs13054
 
13.1%
sgc217
 
0.2%

Length

2025-10-06T12:06:05.073867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:05.614295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
psa86729
86.7%
bgs13054
 
13.1%
sgc217
 
0.2%

Most occurring characters

ValueCountFrequency (%)
s100000
33.3%
p86729
28.9%
a86729
28.9%
g13271
 
4.4%
b13054
 
4.4%
c217
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s100000
33.3%
p86729
28.9%
a86729
28.9%
g13271
 
4.4%
b13054
 
4.4%
c217
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s100000
33.3%
p86729
28.9%
a86729
28.9%
g13271
 
4.4%
b13054
 
4.4%
c217
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s100000
33.3%
p86729
28.9%
a86729
28.9%
g13271
 
4.4%
b13054
 
4.4%
c217
 
0.1%

card_id
Text

Distinct13270
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:06.414949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length229
Median length109
Mean length44.75507
Min length26

Characters and Unicode

Total characters4475507
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7073 ?
Unique (%)7.1%

Sample

1st rowdeandre ayton_panini donruss optic_nan_157
2nd rowluka doncic_panini prizm_silver prizm_280
3rd rowshai gilgeous-alexander_panini donruss optic_shock_162
4th rowtim hardaway_panini prizm fast break autographs_gold prizm_28
5th rowlebron james_panini prizm get hyped!_nan_4
ValueCountFrequency (%)
luka24105
 
5.5%
doncic_panini24014
 
5.4%
trae14970
 
3.4%
young_panini14924
 
3.4%
donruss13112
 
3.0%
jr._panini12045
 
2.7%
prizm_silver9943
 
2.3%
prizm7053
 
1.6%
michael6364
 
1.4%
lebron6356
 
1.4%
Other values (10714)307946
69.9%
2025-10-06T12:06:06.951005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n480102
 
10.7%
i446496
 
10.0%
340833
 
7.6%
a324619
 
7.3%
_300000
 
6.7%
r282181
 
6.3%
e262128
 
5.9%
p232432
 
5.2%
o212199
 
4.7%
s159898
 
3.6%
Other values (38)1434619
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4475507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n480102
 
10.7%
i446496
 
10.0%
340833
 
7.6%
a324619
 
7.3%
_300000
 
6.7%
r282181
 
6.3%
e262128
 
5.9%
p232432
 
5.2%
o212199
 
4.7%
s159898
 
3.6%
Other values (38)1434619
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4475507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n480102
 
10.7%
i446496
 
10.0%
340833
 
7.6%
a324619
 
7.3%
_300000
 
6.7%
r282181
 
6.3%
e262128
 
5.9%
p232432
 
5.2%
o212199
 
4.7%
s159898
 
3.6%
Other values (38)1434619
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4475507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n480102
 
10.7%
i446496
 
10.0%
340833
 
7.6%
a324619
 
7.3%
_300000
 
6.7%
r282181
 
6.3%
e262128
 
5.9%
p232432
 
5.2%
o212199
 
4.7%
s159898
 
3.6%
Other values (38)1434619
32.1%

year_sold
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2021
54945 
2020
28734 
2019
9572 
2022
6679 
2018
 
70

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters400000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2019
3rd row2021
4th row2019
5th row2020

Common Values

ValueCountFrequency (%)
202154945
54.9%
202028734
28.7%
20199572
 
9.6%
20226679
 
6.7%
201870
 
0.1%

Length

2025-10-06T12:06:07.066822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:07.144773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
202154945
54.9%
202028734
28.7%
20199572
 
9.6%
20226679
 
6.7%
201870
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2197037
49.3%
0128734
32.2%
164587
 
16.1%
99572
 
2.4%
870
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2197037
49.3%
0128734
32.2%
164587
 
16.1%
99572
 
2.4%
870
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2197037
49.3%
0128734
32.2%
164587
 
16.1%
99572
 
2.4%
870
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2197037
49.3%
0128734
32.2%
164587
 
16.1%
99572
 
2.4%
870
 
< 0.1%

month_sold
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.46836
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:07.235893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5727953
Coefficient of variation (CV)0.55234949
Kurtosis-1.2156892
Mean6.46836
Median Absolute Deviation (MAD)3
Skewness-0.0029549313
Sum646836
Variance12.764867
MonotonicityNot monotonic
2025-10-06T12:06:07.363221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
111169
11.2%
1210698
10.7%
89887
9.9%
68930
8.9%
78289
8.3%
28032
8.0%
117512
7.5%
57490
7.5%
47218
7.2%
97113
7.1%
Other values (2)13662
13.7%
ValueCountFrequency (%)
111169
11.2%
28032
8.0%
37009
7.0%
47218
7.2%
57490
7.5%
68930
8.9%
78289
8.3%
89887
9.9%
97113
7.1%
106653
6.7%
ValueCountFrequency (%)
1210698
10.7%
117512
7.5%
106653
6.7%
97113
7.1%
89887
9.9%
78289
8.3%
68930
8.9%
57490
7.5%
47218
7.2%
37009
7.0%

day_sold
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.86622
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:07.562189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7866032
Coefficient of variation (CV)0.5537931
Kurtosis-1.1882557
Mean15.86622
Median Absolute Deviation (MAD)8
Skewness-0.012152482
Sum1586622
Variance77.204395
MonotonicityNot monotonic
2025-10-06T12:06:07.775224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
223761
 
3.8%
243561
 
3.6%
173541
 
3.5%
233528
 
3.5%
213505
 
3.5%
93485
 
3.5%
203468
 
3.5%
113436
 
3.4%
63433
 
3.4%
73361
 
3.4%
Other values (21)64921
64.9%
ValueCountFrequency (%)
13239
3.2%
23169
3.2%
33248
3.2%
42988
3.0%
53047
3.0%
63433
3.4%
73361
3.4%
83168
3.2%
93485
3.5%
103312
3.3%
ValueCountFrequency (%)
312244
2.2%
303080
3.1%
292821
2.8%
283308
3.3%
273222
3.2%
262992
3.0%
253255
3.3%
243561
3.6%
233528
3.5%
223761
3.8%

days_since_release
Real number (ℝ)

High correlation 

Distinct1168
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1125.1734
Minimum135
Maximum1508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:08.036410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile605
Q1951
median1168
Q31331
95-th percentile1473
Maximum1508
Range1373
Interquartile range (IQR)380

Descriptive statistics

Standard deviation260.51872
Coefficient of variation (CV)0.23153651
Kurtosis-0.21920499
Mean1125.1734
Median Absolute Deviation (MAD)194
Skewness-0.67056557
Sum1.1251734 × 108
Variance67870.001
MonotonicityNot monotonic
2025-10-06T12:06:08.271421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1176389
 
0.4%
1148388
 
0.4%
1115387
 
0.4%
1267386
 
0.4%
1206345
 
0.3%
1237328
 
0.3%
1393325
 
0.3%
1087320
 
0.3%
1414303
 
0.3%
1392296
 
0.3%
Other values (1158)96533
96.5%
ValueCountFrequency (%)
1351
< 0.1%
1961
< 0.1%
2051
< 0.1%
2191
< 0.1%
2252
< 0.1%
2361
< 0.1%
2531
< 0.1%
2611
< 0.1%
2651
< 0.1%
2672
< 0.1%
ValueCountFrequency (%)
15088
 
< 0.1%
15076
 
< 0.1%
15066
 
< 0.1%
15055
 
< 0.1%
150430
 
< 0.1%
15035
 
< 0.1%
150272
 
0.1%
1501218
0.2%
1500181
0.2%
1499194
0.2%

is_holiday_season
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
81790 
1
18210 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
081790
81.8%
118210
 
18.2%

Length

2025-10-06T12:06:08.492360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:08.654038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
081790
81.8%
118210
 
18.2%

Most occurring characters

ValueCountFrequency (%)
081790
81.8%
118210
 
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
081790
81.8%
118210
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
081790
81.8%
118210
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
081790
81.8%
118210
 
18.2%

brand_encoded
Real number (ℝ)

High correlation 

Distinct460
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288.54966
Minimum0
Maximum459
Zeros27
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:08.841148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59
Q1171
median354
Q3354
95-th percentile391
Maximum459
Range459
Interquartile range (IQR)183

Descriptive statistics

Standard deviation110.23799
Coefficient of variation (CV)0.38204165
Kurtosis-0.61841724
Mean288.54966
Median Absolute Deviation (MAD)15
Skewness-0.91366609
Sum28854966
Variance12152.414
MonotonicityNot monotonic
2025-10-06T12:06:09.061412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35443955
44.0%
17111066
 
11.1%
595341
 
5.3%
1564558
 
4.6%
3753636
 
3.6%
2343085
 
3.1%
3852961
 
3.0%
3911629
 
1.6%
4121616
 
1.6%
2021341
 
1.3%
Other values (450)20812
20.8%
ValueCountFrequency (%)
027
< 0.1%
14
 
< 0.1%
25
 
< 0.1%
31
 
< 0.1%
42
 
< 0.1%
52
 
< 0.1%
62
 
< 0.1%
725
< 0.1%
84
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
4591
 
< 0.1%
4581
 
< 0.1%
4571
 
< 0.1%
4561
 
< 0.1%
45528
< 0.1%
4544
 
< 0.1%
4531
 
< 0.1%
4523
 
< 0.1%
4516
 
< 0.1%
4501
 
< 0.1%

variety_encoded
Real number (ℝ)

High correlation 

Distinct1768
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean999.38766
Minimum0
Maximum1767
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:09.291601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile227
Q1974
median974
Q31189
95-th percentile1517
Maximum1767
Range1767
Interquartile range (IQR)215

Descriptive statistics

Standard deviation364.16006
Coefficient of variation (CV)0.36438318
Kurtosis0.12833935
Mean999.38766
Median Absolute Deviation (MAD)115
Skewness-0.37624722
Sum99938766
Variance132612.55
MonotonicityNot monotonic
2025-10-06T12:06:09.546463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97444796
44.8%
151712068
 
12.1%
11923136
 
3.1%
14752149
 
2.1%
6252061
 
2.1%
10871571
 
1.6%
6841349
 
1.3%
1432903
 
0.9%
1176853
 
0.9%
396808
 
0.8%
Other values (1758)30306
30.3%
ValueCountFrequency (%)
02
 
< 0.1%
13
< 0.1%
22
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
71
 
< 0.1%
85
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
1767351
0.4%
17661
 
< 0.1%
17653
 
< 0.1%
17642
 
< 0.1%
17632
 
< 0.1%
17621
 
< 0.1%
17611
 
< 0.1%
176054
 
0.1%
175921
 
< 0.1%
17581
 
< 0.1%

grading_company_encoded
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
1
86729 
0
13054 
2
 
217

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
186729
86.7%
013054
 
13.1%
2217
 
0.2%

Length

2025-10-06T12:06:09.791571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:09.901915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
186729
86.7%
013054
 
13.1%
2217
 
0.2%

Most occurring characters

ValueCountFrequency (%)
186729
86.7%
013054
 
13.1%
2217
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
186729
86.7%
013054
 
13.1%
2217
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
186729
86.7%
013054
 
13.1%
2217
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
186729
86.7%
013054
 
13.1%
2217
 
0.2%

card_number_len
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.49832
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:10.013840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile3
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.70282439
Coefficient of variation (CV)0.2813188
Kurtosis0.71325603
Mean2.49832
Median Absolute Deviation (MAD)0
Skewness-0.48177012
Sum249832
Variance0.49396212
MonotonicityNot monotonic
2025-10-06T12:06:10.158489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
357222
57.2%
232118
32.1%
19813
 
9.8%
5578
 
0.6%
4195
 
0.2%
671
 
0.1%
73
 
< 0.1%
ValueCountFrequency (%)
19813
 
9.8%
232118
32.1%
357222
57.2%
4195
 
0.2%
5578
 
0.6%
671
 
0.1%
73
 
< 0.1%
ValueCountFrequency (%)
73
 
< 0.1%
671
 
0.1%
5578
 
0.6%
4195
 
0.2%
357222
57.2%
232118
32.1%
19813
 
9.8%

card_number_numeric
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct443
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.45751
Minimum0
Maximum18033
Zeros1430
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:10.384915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q140
median134
Q3201
95-th percentile288
Maximum18033
Range18033
Interquartile range (IQR)161

Descriptive statistics

Standard deviation139.0387
Coefficient of variation (CV)0.98290078
Kurtosis5482.3125
Mean141.45751
Median Absolute Deviation (MAD)88
Skewness43.199442
Sum14145751
Variance19331.759
MonotonicityNot monotonic
2025-10-06T12:06:10.740209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2807422
 
7.4%
785464
 
5.5%
1774810
 
4.8%
323056
 
3.1%
1982828
 
2.8%
2792743
 
2.7%
32584
 
2.6%
1842371
 
2.4%
661918
 
1.9%
61802
 
1.8%
Other values (433)65002
65.0%
ValueCountFrequency (%)
01430
1.4%
1838
 
0.8%
2975
 
1.0%
32584
2.6%
4867
 
0.9%
51371
1.4%
61802
1.8%
7508
 
0.5%
8498
 
0.5%
9628
 
0.6%
ValueCountFrequency (%)
180331
 
< 0.1%
180311
 
< 0.1%
6977
 
< 0.1%
69310
 
< 0.1%
6912
 
< 0.1%
68186
0.1%
6756
 
< 0.1%
67336
< 0.1%
6713
 
< 0.1%
6671
 
< 0.1%

num_subjects
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
1
100000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1100000
100.0%

Length

2025-10-06T12:06:10.967016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:11.069820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1100000
100.0%

Most occurring characters

ValueCountFrequency (%)
1100000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1100000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1100000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1100000
100.0%

is_gold
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
98517 
1
 
1483

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
098517
98.5%
11483
 
1.5%

Length

2025-10-06T12:06:11.203980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:11.345535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
098517
98.5%
11483
 
1.5%

Most occurring characters

ValueCountFrequency (%)
098517
98.5%
11483
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
098517
98.5%
11483
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
098517
98.5%
11483
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
098517
98.5%
11483
 
1.5%

is_silver
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
86397 
1
13603 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
086397
86.4%
113603
 
13.6%

Length

2025-10-06T12:06:11.486107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:11.615607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
086397
86.4%
113603
 
13.6%

Most occurring characters

ValueCountFrequency (%)
086397
86.4%
113603
 
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
086397
86.4%
113603
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
086397
86.4%
113603
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
086397
86.4%
113603
 
13.6%

is_prizm
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
68692 
1
31308 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
068692
68.7%
131308
31.3%

Length

2025-10-06T12:06:11.819160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:11.968016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
068692
68.7%
131308
31.3%

Most occurring characters

ValueCountFrequency (%)
068692
68.7%
131308
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
068692
68.7%
131308
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
068692
68.7%
131308
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
068692
68.7%
131308
31.3%

is_auto
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
97651 
1
 
2349

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
097651
97.7%
12349
 
2.3%

Length

2025-10-06T12:06:12.210315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:12.439245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
097651
97.7%
12349
 
2.3%

Most occurring characters

ValueCountFrequency (%)
097651
97.7%
12349
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
097651
97.7%
12349
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
097651
97.7%
12349
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
097651
97.7%
12349
 
2.3%

is_psa
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
1
86729 
0
13271 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
186729
86.7%
013271
 
13.3%

Length

2025-10-06T12:06:12.629300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:12.766766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
186729
86.7%
013271
 
13.3%

Most occurring characters

ValueCountFrequency (%)
186729
86.7%
013271
 
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
186729
86.7%
013271
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
186729
86.7%
013271
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
186729
86.7%
013271
 
13.3%

is_bgs
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
86946 
1
13054 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
086946
86.9%
113054
 
13.1%

Length

2025-10-06T12:06:13.049411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:13.266989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
086946
86.9%
113054
 
13.1%

Most occurring characters

ValueCountFrequency (%)
086946
86.9%
113054
 
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
086946
86.9%
113054
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
086946
86.9%
113054
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
086946
86.9%
113054
 
13.1%

grade_is_high
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
1
95668 
0
 
4332

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
195668
95.7%
04332
 
4.3%

Length

2025-10-06T12:06:13.482063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:13.622999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
195668
95.7%
04332
 
4.3%

Most occurring characters

ValueCountFrequency (%)
195668
95.7%
04332
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
195668
95.7%
04332
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
195668
95.7%
04332
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
195668
95.7%
04332
 
4.3%

grade_x_brand
Real number (ℝ)

High correlation 

Distinct1116
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2750.0158
Minimum0
Maximum4580
Zeros62
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:13.951105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile590
Q11710
median3186
Q33540
95-th percentile3850
Maximum4580
Range4580
Interquartile range (IQR)1830

Descriptive statistics

Standard deviation1076.7088
Coefficient of variation (CV)0.39152823
Kurtosis-0.70404991
Mean2750.0158
Median Absolute Deviation (MAD)354
Skewness-0.83162293
Sum2.7500158 × 108
Variance1159301.9
MonotonicityNot monotonic
2025-10-06T12:06:14.334569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
354024351
24.4%
318615880
15.9%
15395407
 
5.4%
17104398
 
4.4%
15602730
 
2.7%
5902594
 
2.6%
33632511
 
2.5%
37502491
 
2.5%
5312130
 
2.1%
23401688
 
1.7%
Other values (1106)35820
35.8%
ValueCountFrequency (%)
062
0.1%
9.51
 
< 0.1%
103
 
< 0.1%
171
 
< 0.1%
192
 
< 0.1%
202
 
< 0.1%
301
 
< 0.1%
382
 
< 0.1%
42.51
 
< 0.1%
451
 
< 0.1%
ValueCountFrequency (%)
45801
 
< 0.1%
455014
 
< 0.1%
45401
 
< 0.1%
45301
 
< 0.1%
45201
 
< 0.1%
44804
 
< 0.1%
44706
 
< 0.1%
44604
 
< 0.1%
44406
 
< 0.1%
441038
< 0.1%

grade_x_year
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19181.544
Minimum0
Maximum21189
Zeros35
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:14.733285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18162
Q118162
median20180
Q320180
95-th percentile20180
Maximum21189
Range21189
Interquartile range (IQR)2018

Descriptive statistics

Standard deviation1228.1298
Coefficient of variation (CV)0.064026641
Kurtosis23.783401
Mean19181.544
Median Absolute Deviation (MAD)0
Skewness-2.4403791
Sum1.9181544 × 109
Variance1508302.9
MonotonicityNot monotonic
2025-10-06T12:06:14.997855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2018051306
51.3%
1816236324
36.3%
191717999
 
8.0%
161442835
 
2.8%
17153962
 
1.0%
14126277
 
0.3%
12108115
 
0.1%
1513551
 
0.1%
1009045
 
< 0.1%
2118939
 
< 0.1%
Other values (6)47
 
< 0.1%
ValueCountFrequency (%)
035
 
< 0.1%
20181
 
< 0.1%
60541
 
< 0.1%
80726
 
< 0.1%
1009045
 
< 0.1%
110991
 
< 0.1%
12108115
0.1%
131173
 
< 0.1%
14126277
0.3%
1513551
 
0.1%
ValueCountFrequency (%)
2118939
 
< 0.1%
2018051306
51.3%
191717999
 
8.0%
1816236324
36.3%
17153962
 
1.0%
161442835
 
2.8%
1513551
 
0.1%
14126277
 
0.3%
131173
 
< 0.1%
12108115
 
0.1%

log_price
Real number (ℝ)

High correlation 

Distinct11081
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6783898
Minimum0.68813464
Maximum15.203454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:16.731057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.68813464
5-th percentile2.6390573
Q13.7133281
median4.5643482
Q35.5254529
95-th percentile7.1808647
Maximum15.203454
Range14.515319
Interquartile range (IQR)1.8121248

Descriptive statistics

Standard deviation1.3843801
Coefficient of variation (CV)0.29590951
Kurtosis0.62372084
Mean4.6783898
Median Absolute Deviation (MAD)0.90104299
Skewness0.56492652
Sum467838.98
Variance1.9165081
MonotonicityNot monotonic
2025-10-06T12:06:16.881465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6151205171031
 
1.0%
3.9318256331006
 
1.0%
3.713572067938
 
0.9%
3.433987204853
 
0.9%
3.583518938783
 
0.8%
3.828641396771
 
0.8%
4.33073334763
 
0.8%
4.110873864733
 
0.7%
3.295836866729
 
0.7%
5.017279837701
 
0.7%
Other values (11071)91692
91.7%
ValueCountFrequency (%)
0.68813463875
 
< 0.1%
0.69314718061
 
< 0.1%
0.71294980791
 
< 0.1%
0.810930216215
< 0.1%
0.82855181763
 
< 0.1%
0.83290912291
 
< 0.1%
0.91629073191
 
< 0.1%
0.9932517731
 
< 0.1%
1.000631881
 
< 0.1%
1.007957922
 
< 0.1%
ValueCountFrequency (%)
15.203453811
< 0.1%
13.853558471
< 0.1%
13.378169261
< 0.1%
13.256349021
< 0.1%
12.64433081
< 0.1%
12.388398371
< 0.1%
12.225880171
< 0.1%
12.196178861
< 0.1%
11.918797161
< 0.1%
11.910566711
< 0.1%

is_outlier
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
88558 
1
11442 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
088558
88.6%
111442
 
11.4%

Length

2025-10-06T12:06:17.003129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:17.072272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
088558
88.6%
111442
 
11.4%

Most occurring characters

ValueCountFrequency (%)
088558
88.6%
111442
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
088558
88.6%
111442
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
088558
88.6%
111442
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
088558
88.6%
111442
 
11.4%

price_tier
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.48214
Minimum0
Maximum9
Zeros10553
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:17.153623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.878469
Coefficient of variation (CV)0.64220864
Kurtosis-1.2182757
Mean4.48214
Median Absolute Deviation (MAD)2
Skewness0.0020205324
Sum448214
Variance8.2855839
MonotonicityNot monotonic
2025-10-06T12:06:17.243774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
010553
10.6%
610310
10.3%
410275
10.3%
910000
10.0%
29998
10.0%
89980
10.0%
59889
9.9%
39851
9.9%
19598
9.6%
79546
9.5%
ValueCountFrequency (%)
010553
10.6%
19598
9.6%
29998
10.0%
39851
9.9%
410275
10.3%
59889
9.9%
610310
10.3%
79546
9.5%
89980
10.0%
910000
10.0%
ValueCountFrequency (%)
910000
10.0%
89980
10.0%
79546
9.5%
610310
10.3%
59889
9.9%
410275
10.3%
39851
9.9%
29998
10.0%
19598
9.6%
010553
10.6%

sale_count_per_card
Real number (ℝ)

High correlation 

Distinct204
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean587.41312
Minimum1
Maximum4647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:17.360173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median92
Q3623
95-th percentile3156
Maximum4647
Range4646
Interquartile range (IQR)613

Descriptive statistics

Standard deviation1113.4777
Coefficient of variation (CV)1.8955614
Kurtosis6.185903
Mean587.41312
Median Absolute Deviation (MAD)90
Skewness2.612629
Sum58741312
Variance1239832.5
MonotonicityNot monotonic
2025-10-06T12:06:17.511670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17073
 
7.1%
46474647
 
4.6%
24348
 
4.3%
31563156
 
3.2%
33069
 
3.1%
42384
 
2.4%
51865
 
1.9%
17981798
 
1.8%
61746
 
1.7%
81576
 
1.6%
Other values (194)68338
68.3%
ValueCountFrequency (%)
17073
7.1%
24348
4.3%
33069
3.1%
42384
 
2.4%
51865
 
1.9%
61746
 
1.7%
71568
 
1.6%
81576
 
1.6%
91269
 
1.3%
101170
 
1.2%
ValueCountFrequency (%)
46474647
4.6%
31563156
3.2%
17981798
 
1.8%
15371537
 
1.5%
14531453
 
1.5%
12681268
 
1.3%
12581258
 
1.3%
11571157
 
1.2%
11061106
 
1.1%
929929
 
0.9%

avg_price_per_card
Real number (ℝ)

High correlation  Skewed 

Distinct7134
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean459.60025
Minimum0.99
Maximum4006600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:17.656960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.99
5-th percentile23.406111
Q156.155455
median118.11914
Q3280.845
95-th percentile1274.0186
Maximum4006600
Range4006599
Interquartile range (IQR)224.68955

Descriptive statistics

Standard deviation13503.248
Coefficient of variation (CV)29.380418
Kurtosis77782.578
Mean459.60025
Median Absolute Deviation (MAD)79.850339
Skewness266.66338
Sum45960025
Variance1.823377 × 108
MonotonicityNot monotonic
2025-10-06T12:06:17.819967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
593.01295244647
 
4.6%
217.18756343156
 
3.2%
260.67993551798
 
1.8%
301.29991051537
 
1.5%
118.11913971453
 
1.5%
47.637105681268
 
1.3%
72.53407791258
 
1.3%
95.062506481157
 
1.2%
189.75154611106
 
1.1%
827.0827449929
 
0.9%
Other values (7124)81691
81.7%
ValueCountFrequency (%)
0.992
 
< 0.1%
1.251
 
< 0.1%
1.51
 
< 0.1%
1.992
 
< 0.1%
2.011
 
< 0.1%
2.259
< 0.1%
2.261
 
< 0.1%
2.351
 
< 0.1%
2.491
 
< 0.1%
2.51
 
< 0.1%
ValueCountFrequency (%)
40066001
 
< 0.1%
805233.62
< 0.1%
6457501
 
< 0.1%
2400001
 
< 0.1%
1980301
 
< 0.1%
1690002
< 0.1%
1500601
 
< 0.1%
1488301
 
< 0.1%
972001
 
< 0.1%
80612.254
< 0.1%

std_price_per_card
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct5411
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.4743
Minimum0
Maximum330284.49
Zeros7133
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:17.967450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131.974838
median81.776752
Q3225.42614
95-th percentile976.03047
Maximum330284.49
Range330284.49
Interquartile range (IQR)193.4513

Descriptive statistics

Standard deviation2263.9921
Coefficient of variation (CV)7.2686321
Kurtosis10417.984
Mean311.4743
Median Absolute Deviation (MAD)59.722835
Skewness83.239403
Sum31147430
Variance5125660.2
MonotonicityNot monotonic
2025-10-06T12:06:18.113855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07133
 
7.1%
524.50218074647
 
4.6%
228.03850783156
 
3.2%
141.49958751798
 
1.8%
225.42613981537
 
1.5%
101.58995531453
 
1.5%
95.99109871268
 
1.3%
51.776922951258
 
1.3%
89.659764721157
 
1.2%
121.4683231106
 
1.1%
Other values (5401)75487
75.5%
ValueCountFrequency (%)
07133
7.1%
0.0070710678122
 
< 0.1%
0.014142135622
 
< 0.1%
0.091923881554
 
< 0.1%
0.1484924242
 
< 0.1%
0.24041630562
 
< 0.1%
0.27058578436
 
< 0.1%
0.31819805152
 
< 0.1%
0.353553390614
 
< 0.1%
0.36062445842
 
< 0.1%
ValueCountFrequency (%)
330284.48632
 
< 0.1%
199404.11232
 
< 0.1%
75268.736326
< 0.1%
75102.755113
 
< 0.1%
72856.228293
 
< 0.1%
62649.646672
 
< 0.1%
52321.627784
 
< 0.1%
45398.3304810
< 0.1%
39654.775486
< 0.1%
35001.785672
 
< 0.1%
Distinct1153
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Minimum2018-05-16 00:00:00
Maximum2022-02-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-06T12:06:18.293244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:06:18.453907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1080
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Minimum2018-07-25 00:00:00
Maximum2022-02-17 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-06T12:06:18.602763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:06:18.781089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

trend_score
Real number (ℝ)

High correlation  Zeros 

Distinct4023
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050324322
Minimum-1
Maximum1
Zeros7204
Zeros (%)7.2%
Negative42093
Negative (%)42.1%
Memory size781.4 KiB
2025-10-06T12:06:18.931649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.69141375
Q1-0.33186703
median0.029303299
Q30.45554031
95-th percentile0.87255064
Maximum1
Range2
Interquartile range (IQR)0.78740734

Descriptive statistics

Standard deviation0.48238403
Coefficient of variation (CV)9.5855047
Kurtosis-0.67977668
Mean0.050324322
Median Absolute Deviation (MAD)0.36686916
Skewness0.058623866
Sum5032.4322
Variance0.23269435
MonotonicityNot monotonic
2025-10-06T12:06:19.089336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07204
 
7.2%
0.53876673624647
 
4.6%
0.036200590013156
 
3.2%
12225
 
2.2%
0.14327753011798
 
1.8%
-11769
 
1.8%
0.13210662421537
 
1.5%
-0.34810999381453
 
1.5%
-0.50739727951268
 
1.3%
-0.21840939361258
 
1.3%
Other values (4013)73685
73.7%
ValueCountFrequency (%)
-11769
1.8%
-1104
 
0.1%
-12
 
< 0.1%
-0.99993695523
 
< 0.1%
-0.99993644043
 
< 0.1%
-0.99992565083
 
< 0.1%
-0.99990407293
 
< 0.1%
-0.99989067053
 
< 0.1%
-0.99977061743
 
< 0.1%
-0.99976299564
 
< 0.1%
ValueCountFrequency (%)
12225
2.2%
1142
 
0.1%
0.99998848853
 
< 0.1%
0.99998205883
 
< 0.1%
0.99997179223
 
< 0.1%
0.99997149213
 
< 0.1%
0.99996952513
 
< 0.1%
0.99996931113
 
< 0.1%
0.99996194313
 
< 0.1%
0.99994597783
 
< 0.1%

days_between_sales
Real number (ℝ)

High correlation  Zeros 

Distinct1114
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean807.72082
Minimum0
Maximum1208
Zeros7148
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-10-06T12:06:19.264742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1612
median963
Q31098
95-th percentile1164
Maximum1208
Range1208
Interquartile range (IQR)486

Descriptive statistics

Standard deviation355.61559
Coefficient of variation (CV)0.44027043
Kurtosis-0.036049161
Mean807.72082
Median Absolute Deviation (MAD)158
Skewness-1.0622454
Sum80772082
Variance126462.45
MonotonicityNot monotonic
2025-10-06T12:06:19.418590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07148
 
7.1%
11664647
 
4.6%
11123180
 
3.2%
11191856
 
1.9%
11381807
 
1.8%
11141769
 
1.8%
10971666
 
1.7%
10611579
 
1.6%
11221453
 
1.5%
10241382
 
1.4%
Other values (1104)73513
73.5%
ValueCountFrequency (%)
07148
7.1%
120
 
< 0.1%
28
 
< 0.1%
34
 
< 0.1%
414
 
< 0.1%
521
 
< 0.1%
625
 
< 0.1%
722
 
< 0.1%
818
 
< 0.1%
932
 
< 0.1%
ValueCountFrequency (%)
120879
 
0.1%
11664647
4.6%
1164647
 
0.6%
115725
 
< 0.1%
1153919
 
0.9%
11509
 
< 0.1%
114186
 
0.1%
1140318
 
0.3%
11381807
 
1.8%
1137129
 
0.1%

is_trending_up
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
1
50703 
0
49297 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
150703
50.7%
049297
49.3%

Length

2025-10-06T12:06:19.545905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:19.622202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
150703
50.7%
049297
49.3%

Most occurring characters

ValueCountFrequency (%)
150703
50.7%
049297
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
150703
50.7%
049297
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
150703
50.7%
049297
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
150703
50.7%
049297
49.3%

is_trending_down
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
57907 
1
42093 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
057907
57.9%
142093
42.1%

Length

2025-10-06T12:06:19.707673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T12:06:19.778702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
057907
57.9%
142093
42.1%

Most occurring characters

ValueCountFrequency (%)
057907
57.9%
142093
42.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
057907
57.9%
142093
42.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
057907
57.9%
142093
42.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
057907
57.9%
142093
42.1%

Interactions

2025-10-06T12:05:49.427342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:04:25.569367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:04:28.804405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:04:31.471081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:04:34.132304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:04:37.622231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:04:40.450183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-06T12:05:14.790324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:05:21.543638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:05:30.157752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-06T12:05:42.737309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:05:45.593495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-06T12:05:49.268176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-06T12:06:19.906616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Unnamed: 0Unnamed: 0.1avg_price_per_cardbrand_encodedcard_number_lencard_number_numericday_solddays_between_salesdays_since_releasegradegrade_is_highgrade_x_brandgrade_x_yeargrading_companygrading_company_encodedis_autois_bgsis_goldis_holiday_seasonis_outlieris_prizmis_psais_silveris_trending_downis_trending_uplog_pricemonth_soldpriceprice_tiersale_count_per_cardstd_price_per_cardtrend_scorevariety_encodedyear_sold
Unnamed: 01.0001.000-0.002-0.0010.002-0.0000.003-0.0000.002-0.0030.014-0.001-0.0030.0000.0000.0060.0000.0050.0080.0030.0100.0000.0030.0020.000-0.001-0.002-0.001-0.0010.003-0.001-0.006-0.0050.006
Unnamed: 0.11.0001.000-0.002-0.0010.002-0.0000.003-0.0000.002-0.0030.014-0.001-0.0030.0000.0000.0060.0000.0050.0080.0030.0100.0000.0030.0020.000-0.001-0.002-0.001-0.0010.003-0.001-0.006-0.0050.006
avg_price_per_card-0.002-0.0021.0000.0100.0560.1010.0200.393-0.240-0.0110.0000.015-0.0110.0080.0080.0350.0130.0250.0000.0170.0020.0130.0000.0000.0030.820-0.0220.8200.8140.2610.8570.6110.0260.010
brand_encoded-0.001-0.0010.0101.000-0.269-0.231-0.0030.059-0.0100.1130.1010.8930.1130.2040.2040.2400.2870.1330.0110.1210.5220.2870.3220.1590.171-0.002-0.002-0.002-0.0020.0120.0660.0630.0700.079
card_number_len0.0020.0020.056-0.2691.0000.8040.0040.0660.018-0.0540.063-0.234-0.0540.1290.1290.1430.0610.0660.0200.0490.1070.0630.0810.0930.0590.0430.0070.0430.0440.1090.051-0.0380.0190.045
card_number_numeric-0.000-0.0000.101-0.2310.8041.0000.0020.255-0.006-0.0050.000-0.170-0.0050.0110.0110.0000.0080.0000.0000.0000.0000.0070.0000.0000.0000.0680.0050.0680.0690.2940.159-0.0110.0400.084
day_sold0.0030.0030.020-0.0030.0040.0021.000-0.001-0.0050.0080.002-0.0000.0080.0190.0190.0080.0210.0080.0410.0200.0000.0190.0000.0060.0060.0150.0020.0150.015-0.0010.0150.009-0.0080.056
days_between_sales-0.000-0.0000.3930.0590.0660.255-0.0011.000-0.1150.0600.0730.1220.0600.0910.0910.1950.1150.2700.0120.1090.1070.1200.2180.3500.3800.277-0.0200.2770.2760.8560.5710.2500.1810.068
days_since_release0.0020.002-0.240-0.0100.018-0.006-0.005-0.1151.000-0.1040.060-0.062-0.1040.1670.1670.0560.2190.0580.5600.1150.1600.2130.1040.2470.235-0.1980.076-0.198-0.197-0.003-0.198-0.264-0.0430.765
grade-0.003-0.003-0.0110.113-0.054-0.0050.0080.060-0.1041.0000.8800.4601.0000.0390.0390.0620.0500.0090.0000.1400.0880.0520.0550.0380.0520.2640.0010.2640.2630.036-0.0080.0110.0110.083
grade_is_high0.0140.0140.0000.1010.0630.0000.0020.0730.0600.8801.0000.3500.8800.0660.0660.0660.0630.0090.0000.0240.0660.0650.0410.0060.0220.1470.0090.0000.1430.0130.0000.0470.0440.057
grade_x_brand-0.001-0.0010.0150.893-0.234-0.170-0.0000.122-0.0620.4600.3501.0000.4600.1540.1540.2360.2160.0750.0090.1470.5170.2160.3130.1420.1680.104-0.0010.1040.1030.0600.0700.0690.0870.079
grade_x_year-0.003-0.003-0.0110.113-0.054-0.0050.0080.060-0.1041.0000.8800.4601.0000.0390.0390.0620.0500.0090.0000.1400.0880.0520.0550.0380.0520.2640.0010.2640.2630.036-0.0080.0110.0110.083
grading_company0.0000.0000.0080.2040.1290.0110.0190.0910.1670.0390.0660.1540.0391.0001.0000.1251.0000.1160.0260.1520.0101.0000.0290.1790.1270.1350.0710.0070.1260.0700.0230.1890.0990.184
grading_company_encoded0.0000.0000.0080.2040.1290.0110.0190.0910.1670.0390.0660.1540.0391.0001.0000.1251.0000.1160.0260.1520.0101.0000.0290.1790.1270.1350.0710.0070.1260.0700.0230.1890.0990.184
is_auto0.0060.0060.0350.2400.1430.0000.0080.1950.0560.0620.0660.2360.0620.1250.1251.0000.1230.0740.0020.1270.0940.1250.0520.0870.0040.1670.0080.0320.1380.0980.0560.2050.3530.050
is_bgs0.0000.0000.0130.2870.0610.0080.0210.1150.2190.0500.0630.2160.0501.0001.0000.1231.0000.1160.0190.1520.0100.9910.0280.1780.1260.1900.0440.0110.1780.0990.0330.2650.1390.202
is_gold0.0050.0050.0250.1330.0660.0000.0080.2700.0580.0090.0090.0750.0090.1160.1160.0740.1161.0000.0050.0880.0000.1160.0480.0710.0600.1190.0040.0170.0980.0780.0450.1920.2950.046
is_holiday_season0.0080.0080.0000.0110.0200.0000.0410.0120.5600.0000.0000.0090.0000.0260.0260.0020.0190.0051.0000.0050.0000.0220.0040.0190.0190.0641.0000.0000.0620.0120.0000.0240.0090.166
is_outlier0.0030.0030.0170.1210.0490.0000.0200.1090.1150.1400.0240.1470.1400.1520.1520.1270.1520.0880.0051.0000.0690.1490.0270.2620.2590.9270.0360.0170.9370.2060.0590.4020.1340.081
is_prizm0.0100.0100.0020.5220.1070.0000.0000.1070.1600.0880.0660.5170.0880.0100.0100.0940.0100.0000.0000.0691.0000.0100.5770.1580.1770.0840.0160.0000.0850.3100.0090.3190.6900.151
is_psa0.0000.0000.0130.2870.0630.0070.0190.1200.2130.0520.0650.2160.0521.0001.0000.1250.9910.1160.0220.1490.0101.0000.0290.1790.1240.1860.0440.0110.1730.0980.0330.2630.1390.196
is_silver0.0030.0030.0000.3220.0810.0000.0000.2180.1040.0550.0410.3130.0550.0290.0290.0520.0280.0480.0040.0270.5770.0291.0000.0550.0960.0440.0120.0000.0410.2440.0060.2140.7910.098
is_trending_down0.0020.0020.0000.1590.0930.0000.0060.3500.2470.0380.0060.1420.0380.1790.1790.0870.1780.0710.0190.2620.1580.1790.0551.0000.8650.4290.0530.0000.4520.2930.0170.9170.2420.225
is_trending_up0.0000.0000.0030.1710.0590.0000.0060.3800.2350.0520.0220.1680.0520.1270.1270.0040.1260.0600.0190.2590.1770.1240.0960.8651.0000.4540.0540.0030.4770.3300.0201.0000.1490.212
log_price-0.001-0.0010.820-0.0020.0430.0680.0150.277-0.1980.2640.1470.1040.2640.1350.1350.1670.1900.1190.0640.9270.0840.1860.0440.4290.4541.000-0.0391.0000.9950.1670.6640.5000.0110.118
month_sold-0.002-0.002-0.022-0.0020.0070.0050.002-0.0200.0760.0010.009-0.0010.0010.0710.0710.0080.0440.0041.0000.0360.0160.0440.0120.0530.054-0.0391.000-0.039-0.040-0.014-0.017-0.0210.0020.303
price-0.001-0.0010.820-0.0020.0430.0680.0150.277-0.1980.2640.0000.1040.2640.0070.0070.0320.0110.0170.0000.0170.0000.0110.0000.0000.0031.000-0.0391.0000.9950.1670.6640.5000.0110.009
price_tier-0.001-0.0010.814-0.0020.0440.0690.0150.276-0.1970.2630.1430.1030.2630.1260.1260.1380.1780.0980.0620.9370.0850.1730.0410.4520.4770.995-0.0400.9951.0000.1680.6600.4960.0100.123
sale_count_per_card0.0030.0030.2610.0120.1090.294-0.0010.856-0.0030.0360.0130.0600.0360.0700.0700.0980.0990.0780.0120.2060.3100.0980.2440.2930.3300.167-0.0140.1670.1681.0000.472-0.0110.1080.063
std_price_per_card-0.001-0.0010.8570.0660.0510.1590.0150.571-0.198-0.0080.0000.070-0.0080.0230.0230.0560.0330.0450.0000.0590.0090.0330.0060.0170.0200.664-0.0170.6640.6600.4721.0000.5410.0680.013
trend_score-0.006-0.0060.6110.063-0.038-0.0110.0090.250-0.2640.0110.0470.0690.0110.1890.1890.2050.2650.1920.0240.4020.3190.2630.2140.9171.0000.500-0.0210.5000.496-0.0110.5411.0000.0450.149
variety_encoded-0.005-0.0050.0260.0700.0190.040-0.0080.181-0.0430.0110.0440.0870.0110.0990.0990.3530.1390.2950.0090.1340.6900.1390.7910.2420.1490.0110.0020.0110.0100.1080.0680.0451.0000.076
year_sold0.0060.0060.0100.0790.0450.0840.0560.0680.7650.0830.0570.0790.0830.1840.1840.0500.2020.0460.1660.0810.1510.1960.0980.2250.2120.1180.3030.0090.1230.0630.0130.1490.0761.000

Missing values

2025-10-06T12:05:52.294985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-06T12:05:53.114372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-06T12:05:54.066515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0.1Unnamed: 0yearsubjectbrandvarietycard_numberdatepricegradegrading_companycard_idyear_soldmonth_soldday_solddays_since_releaseis_holiday_seasonbrand_encodedvariety_encodedgrading_company_encodedcard_number_lencard_number_numericnum_subjectsis_goldis_silveris_prizmis_autois_psais_bgsgrade_is_highgrade_x_brandgrade_x_yearlog_priceis_outlierprice_tiersale_count_per_cardavg_price_per_cardstd_price_per_cardfirst_salelast_saletrend_scoredays_between_salesis_trending_upis_trending_down
0002018deandre aytonpanini donruss opticNaN1572021-06-2235.009.00psadeandre ayton_panini donruss optic_nan_15720216221268017197413157100001011539.0018162.003.580221243.0626.902019-05-042022-02-13-0.34101601
1112018luka doncicpanini prizmsilver prizm2802019-05-31465.0010.00psaluka doncic_panini prizm_silver prizm_28020195315150354151713280101101013540.0020180.006.14089192551.202922.182018-12-162022-02-110.68115310
2222018shai gilgeous-alexanderpanini donruss opticshock1622021-10-2049.009.00psashai gilgeous-alexander_panini donruss optic_shock_1622021102013880171147513162100001011539.0018162.003.910324075.8862.172019-04-212022-02-10-0.41102601
3332018tim hardawaypanini prizm fast break autographsgold prizm282019-08-2845.009.50bgstim hardaway_panini prizm fast break autographs_gold prizm_28201982860403595910228110100113410.5019171.003.8302237.4910.612019-06-082019-08-281.008110
4442018lebron jamespanini prizm get hyped!NaN42020-06-2642.9910.00psalebron james_panini prizm get hyped!_nan_420206269070363974114100001013630.0020180.003.78025830.5714.562020-04-132022-02-08-0.1966601
5552018luka doncicpanini chroniclesNaN6812021-07-0693.009.00psaluka doncic_panini chronicles_nan_68120217612820599741368110000101531.0018162.004.540481161.0791.912019-11-132022-02-090.0481910
6662018deandre aytonpanini prizmpink ice prizm2792020-05-0975.0010.00psadeandre ayton_panini prizm_pink ice prizm_2792020598590354108713279100101013540.0020180.004.330498123.4171.152019-02-112022-02-050.56109010
7772018luka doncicpanini prizm freshman phenomsNaN232021-12-2817.508.00psaluka doncic_panini prizm freshman phenoms_nan_2320211228145713629741223100001002896.0016144.002.9200623102.8261.312019-02-242022-02-110.19108310
8882018luka doncicpanini chroniclesgold6452020-08-261426.009.00psaluka doncic_panini chronicles_gold_64520208269680595821364511000101531.0018162.007.261911426.000.002020-08-262020-08-260.00000
9992018wendell carter jr.panini prizm rookie signaturesNaN72020-07-0479.9910.00psawendell carter jr._panini prizm rookie signatures_nan_72020749150369974117100001013690.0020180.004.39041588.4936.332020-04-152021-11-140.4057810
Unnamed: 0.1Unnamed: 0yearsubjectbrandvarietycard_numberdatepricegradegrading_companycard_idyear_soldmonth_soldday_solddays_since_releaseis_holiday_seasonbrand_encodedvariety_encodedgrading_company_encodedcard_number_lencard_number_numericnum_subjectsis_goldis_silveris_prizmis_autois_psais_bgsgrade_is_highgrade_x_brandgrade_x_yearlog_priceis_outlierprice_tiersale_count_per_cardavg_price_per_cardstd_price_per_cardfirst_salelast_saletrend_scoredays_between_salesis_trending_upis_trending_down
9999099990999902018michael porter jr.panini prizmred white and blue prizm322020-08-15381.5010.00psamichael porter jr._panini prizm_red white and blue prizm_322020815957035411921232100101013540.0020180.005.9508244135.8599.332019-04-232022-02-080.06102210
9999199991999912018luka doncicpanini prizmsilver prizm2802019-10-21380.009.50bgsluka doncic_panini prizm_silver prizm_280201910216580354151703280101100113363.0019171.005.94089192551.202922.182018-12-162022-02-110.68115310
9999299992999922018collin sextonpanini prizmsilver prizm1702020-03-2836.009.00psacollin sexton_panini prizm_silver prizm_17020203288170354151713170101101013186.0018162.003.6102280169.61210.992019-02-062022-02-090.14109910
9999399993999932018marvin bagley iiipanini prizm freshman phenomsNaN242020-08-3117.009.50bgsmarvin bagley iii_panini prizm freshman phenoms_nan_24202083197303629740224100000113439.0019171.002.8900417.8810.432020-05-222021-06-11-0.4338501
9999499994999942018lebron jamespanini donruss opticholo942019-09-1759.889.00psalebron james_panini donruss optic_holo_94201991762401716841294100001011539.0018162.004.1103101515.16482.142019-05-292022-01-30-0.0697701
9999599995999952018monte morrispanini select concoursetri-color prizm342022-01-1010.509.00psamonte morris_panini select concourse_tri-color prizm_3420221101470038516471234100101013465.0018162.002.4400110.500.002022-01-102022-01-100.00000
9999699996999962018luka doncicpanini donrussNaN1772021-09-29399.0010.00psaluka doncic_panini donruss_nan_17720219291367015697413177100001011560.0020180.005.99081798260.68141.502018-12-312022-02-110.14113810
9999799997999972018trae youngpanini chroniclesbronze1392020-10-1642.009.00psatrae young_panini chronicles_bronze_1392020101610190591841313910000101531.0018162.003.7602537.7828.682020-01-152022-01-21-0.2973701
9999899998999982018mitchell robinsonpanini prizmNaN2272021-12-126.5010.00psamitchell robinson_panini prizm_nan_227202112121441135497413227100001013540.0020180.002.010011429.5529.252019-07-212022-02-09-0.6493401
9999999999999992018devonte' grahampanini prizmsilver prizm2882020-02-01137.9510.00psadevonte' graham_panini prizm_silver prizm_2882020217610354151713288101101013540.0020180.004.9306269123.83100.472019-07-292022-02-09-0.4092601